Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS

Robotic Platform Rabit for Condition Assessment of Concrete Bridge Decks Using Multiple NDE Technologies

Current assessment of concrete bridge decks relies on visual inspection and use of simple nondestructive and destructive evaluations. More advanced, but still manual nondestructive evaluation (NDE) technologies provide more comprehensive assessment. Still, due to a lower speed of data collection and still not automated data analysis and interpretation, they are not used on a regular basis. The development and implementation of a fully autonomous robotic system for condition assessment of concrete bridge decks using multiple nondestructive evaluation (NDE) technologies is described. The system named RABIT (Robotics Assisted Bridge Inspection Tool) resolves issues related to the speed of data collection and analysis. The system concentrates on the characterization of internal deterioration and damage, in particular three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. For those purposes, RABIT implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ultrasonic surface waves (USW) and ground-penetrating radar (GPR). Because the system utilizes multiple probes or large sensor arrays for the four NDE technologies, the spatial resolution of the results is significantly improved. The technologies are used in a complementary way to enhance the overall condition assessment and certainty regarding the detected deterioration. In addition, the system utilizes three high resolution cameras to image the surface of the deck for crack mapping and documentation of previous repairs, and to image larger areas of the bridge for inventory purposes. Finally, the robot’s data visualization platform facilitates an intuitive 3-dimensional presentation of the main three deterioration types and deck surface features

2022 Technical Program

INSPIRE University Transportation Center 2022 Annual MeetingAugust 1-2, 202

2021 Technical Program

INSPIRE University Transportation Center 2020 Annual MeetingAugust 10-11, 202

2020 Technical Program

INSPIRE University Transportation Center 2020 Annual MeetingAugust 3-4, 202

INSPIRE Newsletter Fall 2022

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